IntroductionHippocampal atrophy is frequently observed in neurodegenerative diseases such as Alzheimer’s disease (AD) or hippocampal sclerosis of aging (HS-aging). Volume loss in the hippocampus is described as prodromal stage of dementia and has been associated with AD polygenic risk score (PRS). CA1 and subiculum atrophy have been suggested to be a promising in vivo biomarker for HS-aging. Recent studies suggest that some loci associated with AD may be more related to other brain diseases concomitant with AD. We aimed to find which significant single nucleotide polymorphisms (SNPs) in the latest AD genome wide association studies (GWAS) could be potentially related with early atrophy of specific hippocampal subregions related to HS-aging.Materials and methodsWe used regression models to assess the relation of the AD-PRS, and genome-wide significant AD variants, with CA1 and subiculum volumes assessed by magnetic resonance imaging (MRI) in 1,859 participants without dementia (with mild cognitive impairment or cognitively healthy). Co-regulatory network analyses and over-representation enrichment analyses were conducted to identify biological pathways enriched with co-regulatory networks of genes associated with hippocampal subregion volumes. We meta-analyzed data from seven cohorts to associate their AD-PRS with AD in the presence of concomitant brain pathologies.ResultsReduced volumes of CA1 and subiculum show association with higher levels of AD-PRS and with variant rs5848 in GRN. This variant was enriched in immune-related pathways. AD-PRS showed no significant association with AD pathology alone, but was strongly associated with AD in the presence of concomitant neurodegenerative pathologies, with HS-aging showing the largest effect size.DiscussionSpecific AD-SNPs enriched in immune-brain axis pathways rather than Aβ related processes, were associated with reduced volumes of CA1 and subiculum prior to dementia onset. This supports that AD-PRS may capture genetic susceptibility to concomitant neurodegenerative diseases frequently misdiagnosed as AD. Given that AD-PRS is most strongly associated with AD in cases with HS-aging, and that CA1 and subiculum are promising in vivo biomarker for HS-aging also linked with rs5848 in GRN, our findings are consistent with HS-aging related vulnerability. This insight links early hippocampal subfields atrophy to shared genetic mechanisms and biological pathways of interest.

Recent studies suggest that copy number variants (CNVs) may contribute to the missing heritability of complex diseases such as Alzheimer's disease (AD) and related dementias (ADRD). We performed a CNV analysis using genotyping data (Axiom 815 K Spanish biobank array) from the GR@ACE/DEGESCO dementia dataset (n = 20,067) of the Spanish population. Applying PennCNV and extensive quality control, 8275 controls and 7818 dementia cases were selected for gene-level case/control associations. We identified 43,833 CNVs with deletions (47%) and duplications (53%). No genome-wide significant associations were found, but nominal associations were observed in PKP3-SIGIRR and FBRSL1 loci. CNVs in 2970 genes were exclusive to dementia cases and enriched in vascular-related pathways. Notable findings included 14q11.2 duplication and VPS13B deletions in ADRD cases, the latter confirmed by optical genome mapping. Our findings suggest potential novel genes associated with ADRD in the Spanish population. However, the limited resolution of array-based technologies in detecting CNVs warrants further investigation.

INTRODUCTION:Early Alzheimer's disease (AD) involves subtle cortical changes that may precede atrophy. Magnetic resonance imaging (MRI) microstructural markers may detect earlier pathology than classical morphometry. METHODS:We analyzed cross-sectional MRI and amyloid-β (Aβ) positron emission tomography (PET) data from 1323 non-demented AMYPAD participants. Cortical volume, thickness, gray-white matter contrast (GWC), and mean diffusivity (MD) were related to global Aβ burden and estimated time to Aβ-positivity using regression, correlation, and change-point analyses. RESULTS:Microstructural measures showed stronger age associations than macrostructural measures, whereas all measures were unaffected by apolipoprotein E (APOE) -ε4 carriership. GWC and MD showed minimal overlap with volume and thickness. Higher Aβ burden was most strongly associated with reduced GWC and cortical thinning. Change-point analyses showed GWC alterations preceded Aβ-positivity by several years. DISCUSSION:Cortical microstructural MRI, particularly GWC, changes earlier than atrophy and may serve as an early in vivo marker of AD pathology.

The Amyloid Imaging to Prevent Alzheimer’s Disease Prognostic and Natural History Study (AMYPAD-PNHS) multimodal magnetic resonance imaging (MRI) dataset provides open-access longitudinal MRI data of 2759 cognitively normal or mild cognitive impairment individuals, encompassing (micro-)structural, physiological, and functional MRI sequences from 10 European parent cohorts. Processed and raw images, and image-derived (endo-)phenotypes, are organized in Brain Imaging Data Structure (BIDS) standards and accessible upon request, to enhance generalizability and comparability between future neuroimaging studies. This dataset supports preclinical Alzheimer’s disease (AD) and aging-related research by enabling robust multimodal analyses of neurodegeneration, microvascular pathology, and structural and functional connectivity changes, facilitating advanced investigations into preclinical AD mechanisms, informing early intervention strategies and allowing reproducible and centralized neuroimaging (endo-)phenotyping.

Abstract Cerebrospinal fluid amyloid beta 42, total tau, and phosphorylated tau 181 are well accepted markers of Alzheimer’s disease. These biomarkers better reflect disease pathogenesis compared to clinical diagnosis. Here, we perform a genome wide association study meta-analysis including 18,948 individuals of European ancestry and identify 12 genome-wide significant loci across all three biomarkers, eight of them novel. We replicate the association of biomarkers with APOE , CR1 , GMNC/CCDC50 and C16orf95/MAP1LC3B . Novel loci include BIN1 for amyloid beta and GNA12, MS4A6A, SLCO1A2 with both total tau and phosphorylated tau 181, as well as additional loci on chr. 8, near ANGPT1 and chr. 9 near SMARCA2 . We also demonstrate that these variants have significant association with Alzheimer’s disease risk, disease progression and/or brain amyloidosis. The associated genes are implicated in lipid metabolism independent of APOE , coupled with autophagy and brain volume regulation driven by total tau and phosphorylated tau 181 dysregulation.

Cerebrospinal fluid (CSF) biomarkers are central to Alzheimer's disease (AD) diagnosis and research. However, CSF composition is shaped not only by neurodegeneration, but also by underlying physiological and pathological processes that remain poorly characterized. By integrating multi-omics data from the deeply characterized memory-clinic ACE CSF cohort (N=1,372), the Global Neurodegeneration Proteomics Consortium (N=1,863), and publicly available quantitative trait loci data, we reveal that 73.2-85.9% of the molecular variance in CSF omics data is driven by two main factors: one reflecting CSF turnover rate, and another representing blood-brain barrier (BBB) integrity. CSF turnover mainly determines brain-derived molecules, while BBB damage leads to increased blood-derived protein abundance. CSF turnover/clearance severely impacted core AD biomarker levels, affecting the classification of subjects in the A/T framework. Adjusting biomarker levels for OPCML, a novel reference marker, improved biomarker-based prediction of AD progression and removed confounded associations, revealing a proteomic signature of sporadic AD pathology that closely resembles that of autosomal dominant AD. Finally, using the ACE CSF cohort as discovery (N=1,221) and Knight ADRC as replication (N=1,073), we report a curated AD signature comprising 446 unique proteins. Our findings identify CSF dynamics as a major source of molecular variation, reshaping the interpretation of CSF biomarkers.

Background The accurate identification of individuals at risk of Alzheimer’s disease (AD) through blood-based biomarkers remains challenging. Objectives To evaluate the association between plasma amyloid-beta (Aβ)42/Aβ40 ratio and longitudinal amyloid deposition, clinical progression, brain atrophy and cognitive decline. Design, setting and participants This study extends the Fundació ACE Healthy Brain Initiative (FACEHBI) study (Barcelona, Spain), comprising 200 individuals with subjective cognitive decline (SCD) followed over five years. Measurements Aβ42/Aβ40 ratio was quantified using ABtest-MS, an antibody-free mass-spectrometry (MS) method. Survival analyses compared conversion risks to amyloid-PET positivity and mild cognitive impairment (MCI), in participants classified as low or high Aβ42/Aβ40, based on a cutoff of ≤ 0.241. Linear mixed-effect models evaluated associations of this biomarker with longitudinal changes in amyloid deposition, brain volume, and cognition. Results Low baseline Aβ42/Aβ40 was significantly associated with increased amyloid accumulation (β = 0.257, 95% confidence interval (CI) 0.177–0.336, P < 0.001), and with higher risk of conversion to Aβ-PET positivity (Hazard ratio (HR) = 2.84, 95% CI 1.14–7.04, P = 0.025) and to MCI due to AD (HR = 3.25, 95% CI 1.17–9.01, P = 0.024). It was also linked to decreased hippocampal (β = -1.183, 95% CI -2.154 to -0.211, P = 0.017) and cortical (β = -75.921, 95% CI -151.728 to -0.113, P = 0.050) volumes, and increased ventricular volume (β = 35.175, 95% CI 18.559–51.790, P < 0.001). Moreover, lower baseline levels of Aβ42/Aβ40 were weakly associated with greater worsening in Mini-Mental State Examination and complex associative memory. Conclusions Our findings suggest that the plasma Aβ42/Aβ40 ratio is associated with future amyloid accumulation, brain atrophy, and conversion to prodromal AD in individuals with SCD. This biomarker may help characterize individuals with a higher likelihood of progression and could support earlier and more personalized strategies.

IntroductionEarly detection of Alzheimer’s disease (AD) is critical for timely intervention, particularly during the mild cognitive impairment (MCI) stage. This study aimed to develop and evaluate a multidomain speech analysis framework to support cognitive screening, biomarker prediction within the amyloid, tau and neurodegeneration (ATN) framework, and estimation of cognitive function across the AD continuum.MethodsThis study analyzed speech from 2,320 individuals spanning the cognitive spectrum-including those with subjective cognitive decline (SCD), MCI, and Alzheimer’s disease dementia (ADD)-using three spoken tasks (∼3 min) and extracted multidomain features including acoustic, lexical, syntactic, and semantic features. Machine learning models were trained to classify cognitive status, predict amyloid, tau and neurodegeneration (ATN) biomarker positivity, and estimate scores across six neuropsychological domains.ResultsMultidomain speech models achieved high performance in differentiating cognitive stages, with AUC values of up to 0.94 for SCD vs. ADD and 0.82 for SCD vs. MCI classifications. In biomarker prediction, the models yielded AUCs of 0.71, 0.74, and 0.73 for ATN classification, respectively. Speech-based models also showed strong correlations (up to 0.83) with cognitive function scores. Feature importance analysis revealed that verbal fluency measures were the most predictive. Explainability analyses indicated minimal dependency on age, sex, or education, supporting model fairness.DiscussionThese findings show that multidomain speech features capture clinically and biologically relevant information across the AD continuum, enabling cognitive classification, biomarker prediction, and cognitive estimation. These results underscore the potential of speech analysis as a non-invasive, accessible tool for scalable cognitive screening and early detection of AD. These results underscore the potential of speech analysis as a non-invasive, accessible tool for scalable cognitive screening and early detection of AD.

Poor self-reported sleep quality is associated with cognitive impairment. Alzheimer’s disease (AD) patients present sleep disruptions decades before they start to decline clinically. Similarly, amyloid-β (Aβ) starts accumulating during the preclinical phase of the disease. This study investigated associations between self-reported sleep quality and Aβ burden longitudinally in clinically unimpaired (CU) adults. Four hundred seventeen CU adults from the AMYPAD PNHS cohort were included, with baseline self-reported sleep quality assessments (Pittsburgh Sleep Quality Index, PSQI) and longitudinal Aβ PET scans. Participants were categorized by baseline Aβ levels as negative (A-), grey-zone (GZ), or positive (A+). Linear mixed-effects (LME) models tested the association between baseline sleep quality and Aβ burden over time, including interaction effects with baseline Aβ status. Global PSQI score was not associated with Aβ burden over time in the entire group. However, a significant interaction with baseline Aβ status was found, whereby poorer subjective sleep quality was linked to accelerated Aβ accumulation in GZ participants. Poorer subjective sleep quality is associated with faster Aβ accumulation in CU individuals with intermediate Aβ levels, highlighting sleep as a potential target for early AD prevention and identifying an optimal intervention window.

Plasma protein quantitative trait loci (pQTLs) have been integrated with genetic studies to prioritize proteins implicated in numerous human diseases. However, limited interaction between plasma and the central nervous system decreases the fluid's relevance for neurological disease. We compared the pQTL landscapes between plasma and cerebrospinal fluid (CSF), detecting widespread differences across fluids that translate to the identification and prioritization of proteins and pathways implicated in neurological disorders. Of almost 5000 CSF and plasma pQTLs, fewer than 30% were present in both fluids, demonstrating the importance of cross-context analyses to understand genetic regulation of protein abundance. We identified 427 associations between proteins and risk of 14 neurological traits, including 249 associations that were not found in previous studies. Only 69 of the associations were consistently detected in both fluids, demonstrating the information gained through the analysis of multiple bodily contexts. We further demonstrated that CSF proteogenomics captures more substantial disease overlap (for example, between Alzheimer's disease and dementia with Lewy bodies) and captures trait-relevant biology missed in plasma, including cell death and immune response signatures in Alzheimer's and multiple sclerosis. Through this work, we demonstrated the importance of analyzing less accessible but more trait-relevant contexts to fully understand human disease.

Background Polygenic risk scores for Alzheimer’s disease (AD-PRS) are widely used to estimate genetic susceptibility to AD, but their relationship with the rate of cognitive decline (CD) after clinical onset remains insufficiently characterized. Objectives To examine the association between AD-PRS and longitudinal CD across the AD spectrum and to evaluate the predictive contribution of individual AD-PRS variants. Design Large longitudinal observational study in a single-center cohort, with an external cohort to assess generalizability. Setting Memory clinic cohort from Ace Alzheimer Center Barcelona (Ace) with external cohort using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Participants The study included 7,233 patients from Ace and 863 from ADNI, with a mean follow-up of 5.4 years in Ace and 3.6 years in ADNI. A biomarker sub-cohort included 1075 participants from Ace and 569 from ADNI. Measurements CD was quantified as the annual change in Mini-Mental State Examination (MMSE) scores estimated using linear mixed-effects models. Associations between AD-PRS and longitudinal MMSE trajectories were tested adjusting for clinical and sociodemographic (CSD) variables and APOE genotype. Machine learning models and SHapley Additive exPlanations (SHAP) were used to evaluate the predictive relevance of individual variants. Results Higher AD-PRS was associated with faster CD in the full clinical cohort and in biomarker subset, independently of APOE genotype. AD-PRS was not associated with baseline MMSE. APOE ε4 was associated with lower baseline MMSE and faster CD only in the full clinical sample. Genetic predictors provided limited improvement beyond CSD variables, and model performance showed limited reproducibility across cohorts. Conclusions AD-PRS is associated with longitudinal CD across the AD spectrum. Although polygenic burden contributes to variability in cognitive trajectories, its added predictive value beyond routinely available clinical variables remains modest.

The cerebrospinal fluid (CSF) proteome offers a direct readout of central nervous system (CNS) biology but its genetic architecture remains incompletely defined. We conducted the largest single-site CSF genome-wide association study (GWAS) to date, analysing 7,092 SomaScan proteins in 1,259 individuals. Using a covariate-adjusted model including proteomic PCs and disease status, we identified 1,971 genome-wide significant pQTLs (954 cis, 971 trans), 1,409 of which replicated in an independent CSF dataset. We discovered 264 previously unreported loci, replicated 511 associations, refined 80 known loci, and 265 proxy-based associations. Using a previously published reproducibility framework, we show that robust discovery concentrates in reliable measurements, underscoring the importance of rigorous quality control. Enrichment analyses revealed immune/complement and extracellular matrix biology. Mendelian randomization prioritised causal proteins: PILRA, TREM2, IL34, CR2, SHARPIN and ERBB1 (Alzheimer's disease); BST1 and GPNMB (Parkinson's disease); STX6 (Creutzfeldt Jacobs disease); and ATXN3 and B4GALNT1 (Amyotrophic lateral sclerosis), providing a scalable framework for orthogonal target validation in neurodegeneration.

BACKGROUND:Polypharmacy, potentially inappropriate medications (PIMs), and drugs associated with drug-induced cognitive impairment (DICI) are highly prevalent in older adults and may affect cognitive decline. METHODS:This observational retrospective study included 2379 patients evaluated at Ace Alzheimer Center Barcelona. Medication data were extracted from electronic health records using a large language model (LLM). Polypharmacy (≥5), hyperpolypharmacy (≥10), PIMs, and DICI drugs were analyzed across cognitive stages, Alzheimer's disease biomarkers, ApoE subtype, and estimated glomerular filtration rate. RESULTS:Among patients with cognitive impairment (Clinical Dementia Rating [CDR] ≥ 0.5), 67% presented polypharmacy and 21% hyperpolypharmacy. Higher odds of polypharmacy were observed across the CDR scale, while higher education showed a protective effect. Altered renal function was found in 20.8% of patients with polypharmacy. Indeed, significant differences were found between amyloid beta-positive and negative (Aβ+/Aβ-) subjects and between the whole population and subjects participating in clinical trials. DISCUSSION:Polypharmacy is extremely common in patients attending memory clinics. LLMs could facilitate systematic structured medication reviews.

Aims:Alzheimer's disease (AD) is commonly diagnosed when neuronal damage is already established and irreversible. Achieving an accurate differential diagnosis in the preclinical and mild cognitive impairment (MCI) stage is one of the greatest challenges nowadays. Nanotechnological analysis of plasma extracellular vesicles (pEVs) are gaining attention as a promising tool for the early detection of AD pathology. This study aims to evaluate the proteomic profile of pEVs from patients with MCI and AD dementia to explore their potential as AD screening tools. Methods:pEVs were isolated by ultracentrifugation from 144 patients with MCI A-T-, MCI A+T+, and AD dementia. Nanoparticle tracking analysis and cryo-TEM were used to characterize the pEVs. CSF, serum and pEVs proteomics were carried out by using the multiplex PEA technology of Olink® proteomics, Inflammation and Neurology Explore 384 panels (768 proteins). Results:Characterization results showed that isolated plasma fraction corresponded in shape, size and concentration to EVs. Many pEVs neurology proteins involved in AD pathology significantly correlated (r > ± 0.30, p < 0.05) with their CSF homonyms, but not with their serum's. pEVs' proteome correlated with common AD signatures (CSF Aβ42 and pTau181, plasma pTau181, MMSE, NBACE, and Qalb) showing similar patterns to those observed with CSF biomarkers. Several pEVs neurology proteins didn't exhibit differences between the MCI A+T+ and AD dementia groups, whilst they did with MCI A-T-. Proteins in pEVs showed strong correlations with several measures of brain atrophy in MRI. Several neurology pEV proteins predicted conversion from MCI to AD dementia. Moreover, some of these showed a significant diagnostic accuracy of AD pathology. Conclusion:Preliminary results suggest that EVs biomarker signature could reflect AD pathology in the prodromal stages of AD continuum. However, further experiments are still needed for a better understanding of EVs' role in AD development and pathology dissemination.

Introduction:Ageing is accompanied by gradual biological and cognitive changes that increase vulnerability to chronic diseases and neurodegenerative conditions. As populations age, dementia prevalence continues to rise, highlighting the need for earlier detection and personalised prevention strategies. Against this background, the COMFORTage project, funded by Horizon Europe, brings together a multidisciplinary consortium across 12 countries to advance innovative, scalable solutions for dementia care. By integrating digital platforms, biomarker research, and precision medicine, COMFORTage seeks to develop artificial intelligence (AI)-driven tools that support more precise and adaptive interventions. Central to this effort are the Virtualized AI-Based Healthcare Platform and Patient Digital Twins, which enable personalised monitoring and decision support. Within this framework, Pilot 3 at Ace Alzheimer Center Barcelona focuses on individuals with mild cognitive impairment and mild Alzheimer's disease dementia, evaluating the effects of cognitive and functional stimulation and contributing multimodal data to optimise the AI platform. Methods:Pilot 3 is a randomised, open-label study involving retrospective and prospective datasets. Participants undergo clinical, genetic, neuropsychological, cerebrospinal fluid (CSF) and plasma biomarker assessments, magnetic resonance imaging (MRI), and spontaneous speech analysis. The primary outcomes assess cognitive decline using composite scores from the Neuropsychological Battery used in Ace (NBACE), targeting attention, memory, visuospatial/perceptual functions, executive functions, and language, over a two-year follow-up. Three digital platforms provided by the consortium will be used as cognitive and functional stimulation tools for participants. The intervention's effects on cognitive decline will be evaluated through changes in NBACE composite scores. Secondary objectives include assessing impacts on physical, psychological, social, and functional well-being; examining associations between biological variables and cognitive changes; and analyzing spontaneous speech as a remote, scalable proxy for cognitive status. Discussion:Findings from Pilot 3 will contribute to COMFORTage's broader mission, offering critical insights into the scalability and real-world implementation of AI-powered dementia care solutions. This integrated approach highlights the potential of precision medicine and advanced digital tools to elevate global standards in dementia management. Clinical Trial Registration:identifier NCT07031167.

The emergence of disease-modifying drug therapies is expected to revolutionize the field of Alzheimer's disease (AD). Recent results from anti-amyloid clinical trials highlight the importance of early identification and accurate risk-stratification of individuals in early stages of the disease. In this context, the Amyloid Imaging to Prevent Alzheimer’s Disease (AMYPAD) Prognostic and Natural History Study (PNHS) was established, leveraging existing cohorts to alleviate the burden of recruiting de novo participants. Here, we describe the harmonization and integration efforts of brain imaging, clinical, cognitive, and fluid biomarker data. Access to the data is available through the Alzheimer’s Disease Data Initiative, with additional details provided at https://amypad.eu/data/ The AMYPAD PNHS integrates prospective and historical data from 32 European sites across 10 countries. These sites contribute data from 10 Parent Cohorts (PC), predominantly comprising non-demented at-risk subjects, including EPAD LCS, EMIF-AD (60++ and 90+), ALFA+, FACEHBI, FPACK, UCL-2010-412, Microbiota, DELCODE, and the AMYPAD Diagnostic and Patient Management Study. A meticulous data curation process was implemented, harmonizing metrics and questionnaires through strategies such as recoding into categories, Percentage of Maximum Possible Scores, and z-scores. Expert reviewers at each site conducted PET visual reads. Centralized quantification of static PET images, employing site-specific Gaussian smoothing, yielded harmonized Centiloid values. Parametric modelling of dynamic PET scans was also performed providing metrics such as the distribution volume ratio. The initial data set includes 3366 participants (55% females, 67±8 years), with 2629 having at least one follow-up visit (2.6±1.9 years). Of those, 1618 underwent baseline amyloid PET, 888 with follow-up. The dataset incorporates clinical outcomes, biomarkers, risk factors, and other relevant variables (Figure 1). Distribution of participants based on amyloid PET status at baseline yielded 60% negative (<12CL), 24% grey-zone (12-50CL), and 16% positive (>50CL) cases. The AMYPAD PNHS represents the largest European longitudinal dataset phenotyping individuals at risk of AD-related progression. The consortium is currently evolving into its new phase, namely the Euro-PAD collaborative framework, and the dataset will be expanded in terms of variables (e.g., currently integrating GWAS and advanced MRI data) and number of cohorts. Interested to join, please contact us at https://amypad.eu/

Neurodegenerative diseases (including Alzheimer's disease, Parkinson's disease, Frontotemporal dementia, and Dementia with Lewy bodies) pose diagnostic challenges due to overlapping pathology and clinical heterogeneity. We leveraged proteomic data from more than 21,000 cerebrospinal fluid and plasma samples to develop and validate explainable, boosting-based multi-disease AI classifiers. The models achieved weighted AUCs in the testing datasets of 0.97 for CSF and 0.88 for plasma, equivalent to traditional biomarkers. The model was validated with neuropathological and clinical data, confirming robust generalizability without any retraining. Using zero-shot learning, we classified disease subtypes including autosomal dominant AD and prodromal PD and clarified disease states for those with conflicting clinical information. The model also showed the ability to prioritize cognitively normal individuals at disease risk. This framework enabled the identification and quantification of continuous, individual-level disease probabilities that allow for the quantification of overlap across diseases and co-pathologies within an individual. Through this work, we establish a benchmark computational framework for enhancing diagnostic precision in NDs.

Alzheimer’s disease (AD) stands as the leading cause of dementia worldwide, and projections estimate over 150 million patients by 2050. AD prevalence is notably higher in women, nearly twice that of men, with discernible sex differences in certain risk factors. To enhance our understanding of how sex influences the characteristics of AD patients and its potential impact on the disease trajectory, we conducted a comprehensive analysis of demographic, clinical, cognitive, and genetic data from a sizable and well-characterized cohort of AD dementia patients at a memory clinic in Barcelona, Spain. The study cohort comprised individuals with probable and possible AD dementia with a Clinical Dementia Rating (CDR) score between 1 and 3 diagnosed at the Memory Unit from Ace Alzheimer Center Barcelona, Spain, between 2008 and 2018. We obtained cognitive baseline data and follow up scores for the Mini-Mental State Examination (MMSE), the CDR scale, and the neuropsychological battery used in our center (NBACE). We employed various statistical techniques to assess the impact of sex on cognitive evolution in these dementia patients, accounting for other sex-related risk factors identified through Machine Learning methods. The study cohort comprised a total of 6108 individuals diagnosed with AD dementia during the study period (28.4

Cerebrospinal fluid (CSF) biomarkers are key sources of insight for research and clinical practice in the neurodegeneration field. Here, we used omics data to characterize a physiological source of variability which has a major impact in the concentration of CSF analytes and is rarely accounted for in these studies. We studied 1,372 samples from the ACE Alzheimer Center Barcelona memory clinic, including cognitively unimpaired subjects and patients with mild cognitive impairment or dementia. We analysed CSF lipidomics (Lipometrix, 386 species), proteomics (SomaScan 7K, 2395 species), and genomics data (TOPMed-imputed Affymetrix Axiom 815K Array). We estimated the overall concentration (mean standardized values) and principal components (PCs) of the proteomics and lipidomics data independently. Genome-wide associations (GWAs) were performed using PLINK v2.00a3 adjusting by age, sex and population microstructure. Enrichment analysis was conducted using WebGestalt. Mean standardized intensities and the PC1 (explaining 40% and 60% of the CSF lipidomics and proteomics variance, respectively) were highly intercorrelated (Figure 1). Despite independent from disease groups and progression, these metrics were strongly associated with CSF p -tau181 and Aβ42 levels (Figure 2). GWAs revealed that GMNC , previously associated with CSF p -tau levels and lateral ventricular volume, and linked to multicilliated cell differentiation in the choroid plexus, was associated ( p  <5·10 -08 ) with these metrics in both experiments. Known alleles increasing ventricular volume were negatively associated with the mean intensity and PC1, downregulating a substantial portion of the proteome (Figure 3). Interestingly, we observed a large overlap in both the down- and upregulated proteomic signature of these variants. Enrichment analysis revealed that proteins diluted by ventricular volume-increasing alleles were related to neuronal function, while increased proteins were associated with immune functions, pinpointing CSF production/clearance rates as likely contributors to this phenomenon. These alleles also caused widespread CSF-specific proteome and metabolome downregulation in an external validation cohort ( https://ontime.wustl.edu/ ). We demonstrate that the main source of variability in the CSF is a non-disease related trait traceable to genetic loci and closely related to ventricular volume. Accounting for this physiological variability is essential for accurate interpretation of CSF biomarkers, with broad implications for neurodegeneration research and clinical practice.